Partner, Head of Data Science at Social Capital, jazz guitarist, physicist…

Feb 9, 2016

Diligence at Social Capital, Epilogue: Introducing the 8-ball and “GAAP for Startups”

Over the past couple of months we’ve written about how we approach the quantitative part of doing diligence on potential investments at Social Capital. This covered several things including growth accounting, our approach to cohorts and LTV, and how we think about the depth of engagement in a given product. While these frameworks were introduced in the context of due diligence, we also described their utility in an operational context. Today we’ll do two things: 1) give some insight into what motivated us to write these pieces and 2) introduce a tool that we’ve built to help you conduct these analyses on your own business/product/feature.

The 8-Ball

The genesis of this entire project came about in late-2014 when we collectively came to realize that, in diligence, we were not seeing core product-market fit at a resolution that we were satisfied with. From our collective time at Facebook, we’d grown accustomed to a sophisticated collection of product metrics and we found ourselves wanting to use those same frameworks when evaluating startups. After doing these analyses ad-hoc a few times and significantly expanding the scope to include non-social network metrics we started referring to the whole analytical package as “the magic 8-ball” or more succinctly as “the 8-ball”. The 8-ball consists of the three pieces that have been described in these posts: 1) growth accounting, 2) cohort behavior and 3) distribution of product-market fit all measured for the core unit of value for the business (typically users and/or (possibly recurring) revenue).

In requesting data from potential investments to run the 8-ball analyses, it became clear to us that there would be value in packaging up a tool that would help perform the analyses. For the cohort-LTV analysis in particular, our heat map and LTV trend views were usually new for entrepreneurs and we typically ended up creating them ourselves and doing a lot of explaining along the way.

So we hired an intern from the Waterloo co-op program, Peter Zhang, and had him build version zero of the 8-ball tool to help us produce the analyses. I invite you to check it out here and give it a spin. If you’re coming in to pitch your company to us, feel free to use the tool to help generate the graphs that we’ll invariably ask for in our own diligence process. In it’s current iteration, the tool only addresses the cohort-LTV aspect of the 8-ball. In the future we’ll build out more features to provide the other standard analyses that have been described in this series of posts.

GAAP for Startups?

Beyond it’s usefulness in diligence, the 8-ball actually addresses a broader need in the startup ecosystem, namely, the lack of standardization in metrics and reporting. To elaborate on this, let’s first take a digression into financial metrics. In the world of financial reporting there is a set of standards that are used to communicate the financial health of a company. These standards are known as GAAP (Generally Accepted Accounting Principles) and their existence was driven by the desire from investors to be able to compare and contrast different companies in a standard fashion. There are other metrics beyond GAAP financials that have gained general usage through ecosystem dynamics. For instance, the notion of MAU (monthly active users) was popularized by the growth of Facebook. Prior to Facebook it was standard to measure pageviews or cumulative registrations. When Facebook launched the Platform it popularized the notion of MAU and DAU thus driving them to become standard language across the entire ecosystem of companies and investors. Similarly, MRR (monthly recurring revenue) is a relatively recent concept in the world of technology investing. It was the explosion of cloud driven software-as-a-service such as Salesforce (also in the mid-aughts) that made MRR a standard way of discussing subscription revenue for SaaS businesses.

For very early stage companies the investor is really looking for evidence of product-market fit and GAAP financial metrics don’t have much to say about the nature of that fit. As such, there is currently no standard way to compare the degree of product-market fit across these early companies.

We would propose that the 8-ball analyses that we have articulated in this series of posts comprise a reasonable GAAP-like standard for understanding product-market fit for early stage companies.

The actual emergence of GAAP is, of course, more complicated than what I mentioned above (in particular with regards to the involvement of regulatory bodies, see this paper for a short history on the topic) but the early stage startup equity market is clearly growing fast (especially with the recent enactment of Title III of the JOBS Act) and much more collective time is bound to be spent on assessing early stage product-market fit. We hope that by sharing our views on this topic we’ve helped inform that discussion and hopefully helped you think about your company, product, feature, investment, or portfolio in a slightly different light.

Appendix: One Big Query

In order to use the 8-ball tool, you will need as input a CSV of aggregated data that you produce from your own data store. Most companies store this data in a database of some sort and you’ll need a query to get the data out.

For the vast majority of companies that we work with the relevant data lives in either a transactional database or in a analytics data warehouse that has a SQL interface (most often Amazon Redshift). Piecing together the quantities in growth accounting or pulling together a complete cohort view of the usage can be tricky so we’re providing a complete SQL query below that captures both the growth accounting and the cohort-LTV data.

The query is rather long and is shown below. It’s written in Postgres and so does the heavy lifting in a series of WITH clauses. It also runs on Amazon Redshift. If your version of SQL doesn’t support the WITH clause (i.e. MySQL) you can separate each one out as a temp table. For the purposes of the example, it uses one of the tutorial data sets provided by Mode. You can run this query directly in Mode’s interactive SQL editor on the tutorial data sets that they provide (feel free to sign up for a free account at Mode).

To apply this to your own data, you should only have to modify the first temp table in the WITH clause and choose which of the final queries to use whether you want to output either variant of growth accounting or the cumulative LTV results. The current 8-ball tool only does visualization for the cohort LTV data.